Enterprises, such as producers of goods or services, systematically allocate resources between a variety of produced goods or services. For example, a manufacturer of goods can conduct its operations by allocating a portion of the revenue stream, the working capital, to the production of multiple products, invest another portion into research and development, equipment purchases, or stock purchases, and allocate yet another portion of its revenue stream to payment on debts. Generally, such enterprises wish to allocate their resources to maximize the accompanying revenue, profit and return on investment (ROI).
Often, the marginal cost of production of each product or service varies, as does the selling price for that product. Furthermore, the marginal cost and selling price of a product can be different in various regions. Thus, there can be an optimal allocation of resources which will provide the maximum return on investment to the enterprise. Finding this optimal allocation has been an immensely difficult challenge for enterprise operators.
Known methods for determining the optimal allocation of resources utilize time variant or invariant control theory applied to enterprise production. For instance, see Jati K. Sengupta, Phillip Fanchon, “Control Theory Methods in Economics” (1997). However, in situations where the control system is using a mathematical enterprise model and variables affecting the optimal allocation of resources are themselves functions of time, known control systems tend to be unable to provide complete guidance on the proper allocation of resources.
Another challenge with optimizing, or otherwise controlling, the allocation of resources within an enterprise is achieving usable, accurate modeling of complex dynamical systems or non-stationary processes. When computational tools are applied in areas such as economics, biology, medicine, and the like, where the systems are subject to myriad time-varying influences from numerous, often inter-related, parameters, conventional control system techniques, which involve modeling of the system, tend to fall short for lack of modeling accuracy.
In view of these, as well as other, challenges, a need exists for a practical technological solution for computationally controlling the allocation of resources in an enterprise.